Virginia Quality Birth to Five System (VQB5) Evaluation & Analysis

Import data and packages

library(tidyverse)
library(ggplot2)
library(here)
library(plotly)
library(readxl)
vqb5 <- read_excel("VQB5-Quality-Rating-Data-for-Website---2023-2024.xlsx")

Data cleaning

vqb5_cleaned <- vqb5 |>
  # keep variables relevant with quality ratings and results
  select(`Ready Region Name`, `Site Type`, `Site Name`, `Other Care Options`,  `Quality Rating`, `Interaction Points`, `Curriculum Points`, `Total Points`, `Operating Period`, `Ready Region Name`
         ) |>
  # transform all variable names to lower case and replace spaces with underscores for readability and reproducibility
  rename_with(~ str_replace_all
              (tolower(.), " ", "_")
              )

1. Summarize the number of sites in each quality result category

quality_counts <- vqb5_cleaned |>
  group_by(quality_rating) |>
  summarise(site_count = n()) |>
  mutate(
    percent = round(100 * site_count / sum(site_count), 2)
  )

quality_counts

# A tibble: 3 × 3
  quality_rating       site_count percent
  <chr>                     <int>   <dbl>
1 Exceeds Expectations         79    2.53
2 Meets Expectations         2993   95.8 
3 Needs Support                51    1.63

2. Summarize quality ratings by site type

# summarize quality counts by site type
quality_by_site_type <- vqb5_cleaned |>
  group_by(site_type, quality_rating) |>
  summarise(site_count = n(), .groups = "drop") |>
  group_by(site_type) |>
  mutate(
    percent = round(100 * site_count / sum(site_count), 2),
    quality_rating = factor(
      quality_rating,
      levels = c("Exceeds Expectations", "Meets Expectations", "Needs Support")
    )
  )

quality_by_site_type

# A tibble: 8 × 4
# Groups:   site_type [3]
  site_type       quality_rating       site_count percent
  <chr>           <fct>                     <int>   <dbl>
1 Center          Exceeds Expectations         19    1.25
2 Center          Meets Expectations         1483   98.0 
3 Center          Needs Support                12    0.79
4 Family Day Home Exceeds Expectations         28    3.71
5 Family Day Home Meets Expectations          687   91.1 
6 Family Day Home Needs Support                39    5.17
7 Public School   Exceeds Expectations         32    3.74
8 Public School   Meets Expectations          823   96.3 

# create a plot with custom hover text
p1 <- quality_by_site_type |>
  ggplot(aes(
    x = quality_rating,
    y = site_count,
    fill = quality_rating,
    text = paste0("Sites: ", site_count, "\nPercent: ", percent, "%")
  )) +
  geom_col() +
  facet_wrap(~ site_type) +
  scale_fill_manual(values = c(
    "Exceeds Expectations" = "#1b9e77",
    "Meets Expectations"   = "#7570b3",
    "Needs Support"        = "#d95f02"
  )) +
  labs(
    title = "Quality Ratings by Site Type",
    x = NULL,
    y = "Number of Sites",
    fill = "Quality Rating"
  ) +
  theme_minimal() +
  theme(
    panel.grid.major.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )

# Convert to interactive plotly, showing site count on hover
ggplotly(p1, tooltip = "text", width = 1000, height = 500)

3. Summarize quality ratings by ready region

# Summarize quality counts by ready region name
quality_by_ready_region <- vqb5_cleaned |>
  group_by(ready_region_name, quality_rating) |>
  summarise(region_count = n(), .groups = "drop") |>
  group_by(ready_region_name) |>
  mutate(
    percent = round(100 * region_count / sum(region_count), 2),
    quality_rating = factor(
      quality_rating,
      levels = c("Exceeds Expectations", "Meets Expectations", "Needs Support")
    )
  )

quality_by_ready_region 

# A tibble: 24 × 4
# Groups:   ready_region_name [9]
   ready_region_name          quality_rating       region_count percent
   <chr>                      <fct>                       <int>   <dbl>
 1 Ready Region 1 - Southwest Exceeds Expectations            7    3.26
 2 Ready Region 1 - Southwest Meets Expectations            208   96.7 
 3 Ready Region 2 - West      Exceeds Expectations            2    1.12
 4 Ready Region 2 - West      Meets Expectations            175   97.8 
 5 Ready Region 2 - West      Needs Support                   2    1.12
 6 Ready Region 3 - Southside Exceeds Expectations            8    5.3 
 7 Ready Region 3 - Southside Meets Expectations            143   94.7 
 8 Ready Region 4 - Central   Exceeds Expectations            6    1.45
 9 Ready Region 4 - Central   Meets Expectations            402   97.1 
10 Ready Region 4 - Central   Needs Support                   6    1.45
# ℹ 14 more rows

# Create plot with custom hover text
p2 <- quality_by_ready_region |>
  ggplot(aes(
    x = quality_rating,
    y = region_count,
    fill = quality_rating,
    text = paste0("Sites: ", region_count, "\nPercent: ", percent, "%")
  )) +
  geom_col() +
  facet_wrap(~ ready_region_name) +
  scale_fill_manual(values = c(
    "Exceeds Expectations" = "#1b9e77",
    "Meets Expectations"   = "#7570b3",
    "Needs Support"        = "#d95f02"
  )) +
  labs(
    title = "Quality Ratings by Ready Region",
    x = NULL,
    y = "Number of Sites",
    fill = "Quality Rating"
  ) +
  theme_minimal() +
  theme(
    panel.grid.major.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )

ggplotly(p2, tooltip = "text", width = 1000, height = 600)